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Adaptive testing in arch models Author info | Abstract | Publisher info | Download info | Related research | Statistics Oliver Linton
Douglas Steigerwald
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Specification tests for conditional heteroskedasticity that are derived under the assumption that the density of the innovation is Gaussian may not be powerful in light of the recent empirical results that the density is not Gaussian. We obtain specification tests for conditional heteroskedasticity under the assumption that the innovation density is a member of a general family of densities. Our test statistics maximize asymptotic local power and weighted average power criteria for the general family of densities. We establish both first-order and second-order theory for our procedures. Simulations indicate that asymptotic power gains are achievable in finite samples.
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Article provided by Taylor and Francis Journals in its journal Econometric Reviews .
Volume (Year): 19 (2000)
Issue (Month): 2 ()
Pages: 145-174
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Handle: RePEc:taf:emetrv:v:19:y:2000:i:2:p:145-174Contact details of provider: Web page: http://taylorandfrancis.metapress.com/link.asp?target=journal&id=107830
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For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).
Keywords: adaptive testing ; ARCH ; conditional heteroskedasticity ; semiparametric ; Other versions of this item:
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